3D LiDAR Scanning of Urban Forest Structure Using a Consumer Tablet

Forest measurements using conventional methods may not capture all the important information required to properly characterize forest structure. The objective of this study was to develop a low-cost alternative method for forest inventory measurements and characterization of forest structure using handheld LiDAR technology. Three-dimensional (3D) maps of trees were obtained using an iPad Pro with a LiDAR sensor. Freely-available software programs, including 3D Forest Software and CloudCompare software, were used to determine tree diameter at breast height (DBH) and distance between trees. The 3D point cloud data obtained from the iPad Pro LiDAR sensor was able to estimate tree DBH accurately, with a residual error of 2.4 cm in an urban forest stand and 1.9 cm in an actively managed experimental forest stand. Distances between trees also were accurately estimated, with mean residual errors of 0.21 m for urban forest, and 0.38 m for managed forest stand. This study demonstrates that it is possible to use a low-cost consumer tablet with a LiDAR sensor to accurately measure certain forest attributes, which could enable the crowdsourcing of urban and other forest tree DBH and density data because of its integration into existing Apple devices and ease of use

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

3D LiDAR Scanning of Urban Forest Structure Using a Consumer Tablet

Forest measurements using conventional methods may not capture all the important information required to properly characterize forest structure. The objective of this study was to develop a low-cost alternative method for forest inventory measurements and characterization of forest structure using handheld LiDAR technology. Three-dimensional (3D) maps of trees were obtained using an iPad Pro with a LiDAR sensor. Freely-available software programs, including 3D Forest Software and CloudCompare software, were used to determine tree diameter at breast height (DBH) and distance between trees. The 3D point cloud data obtained from the iPad Pro LiDAR sensor was able to estimate tree DBH accurately, with a residual error of 2.4 cm in an urban forest stand and 1.9 cm in an actively managed experimental forest stand. Distances between trees also were accurately estimated, with mean residual errors of 0.21 m for urban forest, and 0.38 m for managed forest stand. This study demonstrates that it is possible to use a low-cost consumer tablet with a LiDAR sensor to accurately measure certain forest attributes, which could enable the crowdsourcing of urban and other forest tree DBH and density data because of its integration into existing Apple devices and ease of use